Multicentric Language Markers of NeuroDegeneration

By 2030, people aged 65 and over are expected to account for more than 25% of the European population.

This fact foreshadows a dramatic growth of aging-related neurodegenerative disorders (NDs), including Alzheimer's and Parkinson's disease.

Critically, this scenario will place a severe strain on healthcare systems as NDs incapacitate patients, burden their families, and entail major costs of diagnostics evaluation, including time-consuming neuropsychological batteries and expensive brain scans.

Though currently irreplaceable, this approach is often unaffordable and non-viable for remote application -a key requisite during pandemic lockdowns.

Thus, these procedures must be complemented with urgent innovations that boost diagnosis and symptom severity detection using low-cost tools applicable to large sections of the population remotely.

A promising interdisciplinary framework rooted in natural language markers (NLMs) can offer key solutions to this crisis.

This novel framework is based on linguistic features derived from patient's natural speech and analysed via machine learning algorithms.

NLMs are characterized by high ecological validity, minimal stress, low costs and adaptability for remote and massive screening. Despite the increasing application of NLMs in the field of mental health, their use in NDs evaluation is still scarce.

Building on a unique synergy of international expertise, we will perform the first cross-methodological (behavioural, f/MRI, EEG) and cross-centre (Latin-America and Europe) validation of NLMs in patients with NDs.

Specifically, we aim to: (1) establish NLMs diagnostic sensitivity, (2) unveil potential links between NLMs and brain network disruptions, (3) estimate their robustness and generalisation power.

The ultimate translational goal of this project is to identify the best-performing set of NLMs to develop a frontline mobile-phone app with clinical value, capable of capturing natural speech features for remote patient evaluation.